In order to improve the search performance of combinatorial optimization problem for particle swarm optimization,a multi-strategy particle swarm optimization method is proposed for solving permutation flow-shop scheduling problems based on various combinatorial optimization strategies. The proposed method uses information entropy to measure the population diversity of particle group through the sub interval of gravitational value partition. At the same time,the global optimal particle selection is selected by ant routing selection strategy and considering the distance between particles and the mass of inertia. In addition,a novel mutation is designed to guide particle swarm to jump out of the local optimal solution area and enhance the global search ability of particle swarm. The simulation results of test problems show that the multi-strategy particle swarm optimization can effectively accelerate the search performance and convergence speed of the optimal solution,and it can be effectively applied to solve the permutation flow-shop scheduling problem.